2 citations · 3 across the 5 of their papers we have counts for
5 papers
Data-Driven Pixel Control: Challenges and Prospects
Saurabh Farkya, Zachary Alan Daniels, Aswin Raghavan +4
Recent advancements in sensors have led to high resolution and high data throughput at the pixel level. Simultaneously, the adoption of increasingly large (deep) neural networks (N…
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training
Saurabh Farkya, Aswin Raghavan, Avi Ziskind
Most real-world applications that employ deep neural networks (DNNs) quantize them to low precision to reduce the compute needs. We present a method to improve the robustness of qu…
Low-power In-pixel Computing with Current-modulated Switched Capacitors
David Zhang, Gooitzen van der Wal, Saurabh Farkya +4
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend. Unlike the state…
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels +8
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at th…
Saccade Mechanisms for Image Classification, Object Detection and Tracking
Saurabh Farkya, Zachary Daniels, Aswin Nadamuni Raghavan +2
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed a…